Method and apparatus for training bilingual word alignment model, method and apparatus for bilingual word alignment

ABSTRACT

The present invention provides method and apparatus for bilingual word alignment, method and apparatus for training bilingual word alignment model. The method for training bilingual word alignment model, comprising: training a bilingual word alignment model for a first language and a second language, using a bilingual corpus of the first and second languages; training a bilingual word alignment model for the second language and a third language, using a bilingual corpus of the second and third languages; and estimating a bilingual word alignment model for the first language and the third language, based on said bilingual word alignment model for the first and second languages and said bilingual word alignment model for the second and third languages.

TECHNICAL FIELD

The present invention relates to information processing technology,specifically to the technology of bilingual word alignment and thetechnology of statistical machine translation in natural languageprocessing.

TECHNICAL BACKGROUND

Word alignment is widely used in natural language processing. Existingword alignment technology usually uses a statistical word alignmentmodel to align the corresponding words in a bilingual sentence pair. Thestatistical word alignment model contains statistical information usedfor determining the corresponding words in a bilingual sentence pair.

In the article by P. F. Brown, S. A. Della Pietra, V. J. Della Pietraand R. Mercer published in 1993, “The Mathematics of Statistical MachineTranslation: Parameter Estimation” (Computational Linguistics, 19(2):263-311), a statistical machine translation model and a statistical wordalignment model as well as corresponding parameter estimation method aredescribed.

The statistical word alignment model needs a large enough bilingualcorpus to train the parameters. If there is no large enough corpus fortraining, it is impossible to produce alignment result with high qualityby using the obtained parameters. However, for some languages, availablebilingual corpus is still less, so the amount of bilingual corpus limitsthe quality of the statistical word alignment model and becomes anobstacle to the further application of the statistical word alignmentmodel.

SUMMARY OF THE INVENTION

In order to solve above-mentioned problems of the prior technology, thepresent invention provides a method and apparatus for training abilingual word alignment model using an intermediate language as well asa method and apparatus for bilingual word alignment.

According to one aspect of the present invention, there is provided amethod for training a bilingual word alignment model, comprising:training a bilingual word alignment model for a first language and asecond language, using a bilingual corpus of the first and secondlanguages; training a bilingual word alignment model for the secondlanguage and a third language, using a bilingual corpus of the secondand third languages; and estimating a bilingual word alignment model forthe first language and the third language, based on said bilingual wordalignment model for the first and second languages and said bilingualword alignment model for the second and third languages.

According to another aspect of the present invention, there is provideda method for bilingual word alignment, comprising: obtaining a bilingualword alignment model for a first language and a third language based onthe bilingual corpus of the first and second languages and the bilingualcorpus of the second and third languages, by using the above describedmethod for training a bilingual word alignment model; word-aligning abilingual sentence pair of the first and third languages using saidbilingual word alignment model of the first and third languages.

According to another aspect of the present invention, there is providedan apparatus for training a bilingual word alignment model, comprising:a first training unit configured to train a bilingual word alignmentmodel for a first language and a second language, using a bilingualcorpus of the first and second languages; a second training unitconfigured to train a bilingual word alignment model for the secondlanguage and a third language, using a bilingual corpus of the secondand third languages; and a model estimating unit configured to estimatea bilingual word alignment model for the first language and the thirdlanguage, based on said bilingual word alignment model for the first andsecond languages and said bilingual word alignment model for the secondand third languages.

According to another aspect of the present invention, there is providedan apparatus for bilingual word alignment comprising: a model obtainingunit configured to obtain a bilingual word alignment model for a firstlanguage and a third language based on a the bilingual corpus of thefirst and second languages and the bilingual corpus of the second andthird languages by the above described apparatus for training abilingual word alignment model; and a word-alignment unit configured toword-align a bilingual sentence pair of the first and third languagesusing the bilingual word alignment model for the first and thirdlanguages.

BRIEF DESCRIPTION OF THE DRAWINGS

It is believed that above-mentioned features, advantages and objectivesof the present invention will be better understood through followingdescription of the embodiments of the invention, taken in conjunctionwith the drawings in which,

FIG. 1 is a flowchart showing a method for training a bilingual wordalignment model according to an embodiment of the present invention;

FIG. 2 is a flowchart showing a method for bilingual word alignmentaccording to an embodiment of the present invention;

FIG. 3 is a block diagram showing an apparatus for training a bilingualword alignment model according to an embodiment of the presentinvention; and

FIG. 4 is a block diagram showing an apparatus for bilingual wordalignment according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Next, a detailed description of the preferred embodiments of the presentinvention will be given in conjunction with the drawings.

FIG. 1 is a flowchart showing a method for training a bilingual wordalignment model according to an embodiment of the present invention;

As shown in FIG. 1, first in Step 101, the bilingual corpus between thefirst and second languages is used to train a bilingual word alignmentmodel for the first and second languages. In this embodiment, thebilingual word alignment model includes a word translation sub-model, aposition distortion sub-model and a word fertility sub-model.

In these sub-models, the word translation sub-model is a set of wordtranslation probabilities. A word translation probability p(w_(s)/w_(t))is the translation probability from the target word w_(t) to the sourceword w_(s).

The position distortion sub-model is a set of position distortionprobabilities. A position distortion probability p(j/i,l,m) is theprobability of selecting the j^(th) position in the sentence in sourcelanguage, given the i^(th) position in the sentence in target language,the length m of the sentence in source language, and the length l of thesentence in target language.

The word fertility sub-model is a set of word fertility probabilities. Aword fertility probability p(φ_(i)/w_(t)) is the probability of thetarget word w_(t) aligning φ_(i) source words.

In this step, using the statistical method, based on the bilingualcorpus of the first and second languages, a bilingual word alignmentmodel, i.e., a word translation sub-model, a position distortionsub-model and a word fertility sub-model for the first and secondlanguages is trained.

Next, in Step 105, the bilingual corpus of the second and thirdlanguages is used to train a bilingual word alignment model for thesecond and third languages. Similar to the above Step 101, in this step,using the statistical method, based on the bilingual corpus of thesecond and third languages, a bilingual word alignment model, i.e., aword translation sub-model, a position distortion sub-model and a wordfertility sub-model for the second and third languages is trained.

In this embodiment, it is supposed that a large-scale accurate bilingualcorpus between the first and second languages and between the second andthird languages is available, but the bilingual corpus between the firstand third languages is lack. Thus, through the above Steps 101 and 105,sufficient bilingual corpus between the first and second languages andbetween the second and third languages may be used to obtain a bilingualword alignment model with good quality for the first and secondlanguages and for the second and third languages.

Next, in Step 110, based on the bilingual word alignment model for thefirst and second languages and the bilingual word alignment model forthe second and third languages, a bilingual word alignment model for thefirst and third languages is estimated.

In this embodiment, it is needed to estimate a word translationsub-model, a position distortion sub-model and a word fertilitysub-model respectively, specifically, including following steps

estimating a word translation sub-model for the first and thirdlanguages, based on the word translation sub-model for the first andsecond languages and the word translation sub-model for the second andthird languages;

estimating a position distortion sub-model for the first and thirdlanguages, based on the position distortion sub-model for the first andsecond languages and the position distortion sub-model for the secondand third languages; and

estimating a word fertility sub-model for the first and third languages,based on the word fertility sub-model for the first and second languagesand/or the word fertility sub-model for the second and third languages,the word translation sub-model for the first and second languages and/orthe word translation sub-model for the second and third languages.

Next, a detailed description will be given to the estimation process ofthe above-mentioned sub-models.

1) First, as to the estimation of a word translation sub-model for thefirst and third languages

Suppose that p_(CE)(w_(c)|w_(e)) represents the translation probabilityfrom the second language word w_(e) to the first language word w_(c),p_(EJ)(w_(e)|w_(j)) represents the translation probability from thethird language word w_(j) to the second language word w_(e),C(w_(j),w_(c)) represents the co-occurrence count of the first languageword w_(c) and the third language word w_(j), p(w_(c)|w_(j)) representsthe translation probability from the third language word w_(j) to thefirst language word w_(c),

collecting the co-occurrence count of the first language word w_(c) andthe third language word w_(j), using formula

${{C\left( {w_{j},w_{c}} \right)} = {\sum\limits_{w_{c}}\; {{p_{EJ}\left( {w_{e}\text{|}w_{j}} \right)}*{p_{CE}\left( {w_{c}\text{|}w_{e}} \right)}}}};$

and

calculating the translation probability from the third language wordw_(j) to the first language word w_(c), using formula

${p\left( {w_{c}\text{|}w_{j}} \right)} = {\frac{C\left( {w_{j},w_{c}} \right)}{\sum\limits_{w_{c^{\prime}}}\; {C\left( {w_{j},w_{c^{\prime}}} \right)}}.}$

2) Next, as to the estimation of a position distortion sub-model for thefirst and third languages

Suppose that p_(EJ)(k|i,l,m′) represents the probability that the i^(th)position in the third language sentence having a length of l iscorresponding to the k^(th) position in the second language sentencehaving a length of m′, p_(CE)(j|k,m′,m) represents the probability thatthe k^(th) position in the second language sentence having a length ofm′ is corresponding to the j^(th) position in the first languagesentence having a length of m, C(j,i,l,m) and P_(CJ)(j|i,l,m)respectively represent the co-occurrence count and probability that thei^(th) position in the third language sentence having a length of l iscorresponding to the j^(th) position in the first language sentencehaving a length of m,

collecting the co-occurrence count that the i^(th) position in the thirdlanguage sentence having a length of l is corresponding to the j^(th)position in the first language sentence having a length of in, usingformula C(j,i,l,m)=Σ_(k,m′)p_(EJ)(k|i,l,m′)*p_(CE)(j|k,m′,m); and

calculating the position distortion probability that the i^(th) positionin the third language sentence having a length of l is corresponding tothe j^(th) position in the first language sentence having a length of m,using formula

${p_{CJ}\left( {{j\text{|}i},l,m} \right)} = {\frac{C\left( {j,i,l,m} \right)}{\sum\limits_{j^{\prime}}\; {C\left( {j^{\prime},i,l,m} \right)}}.}$

3) Finally, as to the estimation of a word fertility sub-model for thefirst and third languages

Suppose that p_(EJ)(w_(e)|w_(j)) represents the translation probabilityform the third language word w_(j) to the second language word w_(e),P_(CE)(φ_(i)|w_(e)) represents the probability that the second languageword w_(e) is corresponding to φ_(i) words of the first language,C(φ_(i),w_(j)) and p(φ_(i)|w_(j)) respectively represent theco-occurrence count and probability that the third language word w_(j)is corresponding to φ_(i) words of the first language,

collecting the co-occurrence count that the third language word w_(j) iscorresponding to φ_(i) words of the first language, using formula

${{C\left( {\phi_{i},w_{j}} \right)} = {\sum\limits_{w_{e}}\; {{p_{EJ}\left( {w_{e}\text{|}w_{j}} \right)}*{p_{CE}\left( {\phi_{i}\text{|}w_{e}} \right)}}}};$

and

calculating the probability that the third language word w_(j) iscorresponding to φ_(i) words of the first language, using formula

${p\left( {\phi_{i}\text{|}w_{j}} \right)} = {\frac{C\left( {\phi_{i},w_{j}} \right)}{\sum\limits_{\phi_{i}^{\prime}}\; {C\left( {\phi_{i}^{\prime},w_{j}} \right)}}.}$

From the above description it can be seen that the method for training abilingual word alignment model of this embodiment may use anintermediate language to solve the problem that there is no way toobtain a word alignment model with high quality due to not sufficientcorpus for training. For instance, usually there is not enough bilingualcorpus between Chinese and Japanese, which limits the quality of astatistical word alignment model for Chinese and Japanese. By using themethod of this embodiment, an intermediate language with a large-scalecorpus, such as English, can be used to solve this problem. Becauselarge-scale bilingual corpus between Chinese and English and large-scalecorpus between Japanese and English are available, a word alignmentmodel with high quality for Chinese and English and a word alignmentmodel with high quality for Japanese and English can be obtained and aword alignment model for Chinese and Japanese can be further estimatedby using the word alignment model for Chinese and English and the wordalignment model for Japanese and English.

Of course, the present invention is not limited to the case of Chinese,English and Japanese, and any language may be used as the first, secondand third language in the previous embodiments. However, usually thoseinternational languages with large-scale corpus should be considered,such as English, French and Spanish.

Under the same inventive concept, FIG. 2 is a flowchart showing a methodfor bilingual word alignment according to an embodiment of the presentinvention. Next, in conjunction with the figure, a description will begiven to this embodiment. For the parts identical to that in theprevious embodiment, explanation will be omitted properly.

As shown in FIG. 2, first in Step 101, a bilingual corpus of a firstlanguage and a second language is used to train a bilingual wordalignment model for the first and second languages. Then, in Step 105, abilingual corpus of the second language and a third language is used totrain a bilingual word alignment model for the second and thirdlanguages. Then, in Step 110, based on said bilingual word alignmentmodel for the first and second languages and said bilingual wordalignment model for the second and third languages, a bilingual wordalignment model for the first language and the third language isestimated.

Above steps 101, 105 and 110 are basically the same as that in theembodiment shown in FIG. 1 and not repeated here.

Then, in Step 215, the estimated bilingual word alignment model for thefirst and third languages is used to word-align the bilingual sentencesin the first and third languages. Specific alignment manner is:

1. The word translation probability and the position alignmentprobability are used to find an optimal word alignment for each sourcelanguage word so as to obtain an alignment series A0.

2. On the basis of the alignment series Ai, the word translationprobability, a position distortion model and a word fertility model areused to find a better alignment series Ai+1 through trying exchangingany two alignments or changing an alignment.

3. The process 2 is repeated till no better alignment series is found.

Here, those skilled in the art should understand that any known andfuture searching algorithms can be used to search an optimal alignmentseries.

From above description it can be seen that the method for bilingual wordalignment of this embodiment may use an intermediate language to solvethe problem that there is no way to obtain a word alignment model withhigh quality due to not sufficient corpus for training. Thus, even forthose bilingual languages with less corpus, such as Chinese and English,accurate word alignment can be made.

Under the same inventive concept, FIG. 3 is a block diagram showing anapparatus for training a bilingual word alignment model according to anembodiment of the present invention. Next, in conjunction with thefigure, a description will be given to this embodiment. For the partsidentical to that in the previous embodiments, explanation will beomitted properly.

As shown in FIG. 3, the apparatus 300 for training a bilingual wordalignment model of this embodiment includes: a first training unit 303configured to train a bilingual word alignment model for a firstlanguage and a second language, using a bilingual corpus 301 of thefirst and second languages; a second training unit 304 configured totrain a bilingual word alignment model for the second language and athird language, using a bilingual corpus 302 of the second and thirdlanguages; and a model estimating unit 305 configured to estimate abilingual word alignment model for the first language and the thirdlanguage, based on said bilingual word alignment model for the first andsecond languages trained by the first training unit 303 and saidbilingual word alignment model for the second and third languagestrained by the second training unit 304.

Specifically, said bilingual word alignment model for the first andsecond languages trained by the first training unit 303 and saidbilingual word alignment model for the second and third languagestrained by the second training unit 304 respectively comprises: a wordtranslation sub-model, a position distortion sub-model and a wordfertility sub-model. Said model estimating unit comprises: a wordtranslation sub-model estimating unit configured to estimate a wordtranslation sub-model for the first and third languages, based on theword translation sub-model for the first and second languages and theword translation sub-model for the second and third languages; aposition distortion sub-model estimating unit configured to estimate aposition distortion sub-model for the first and third languages, basedon the position distortion sub-model for the first and second languagesand the position distortion sub-model for the second and thirdlanguages; and a word fertility sub-model estimating unit configured toestimate a word fertility sub-model for the first and third languages,based on the word fertility sub-model for the first and second languagesand/or the word fertility sub-model for the second and third languages,the word translation sub-model for the first and second languages and/orthe word translation sub-model for the second and third languages.

Similar to the previous embodiment, in this embodiment, suppose thatP_(CE)(w_(c)|w_(e)) represents the translation probability from thesecond language word w_(e) to the first language word w_(c),p_(EJ)(w_(e)|w_(j)) represents the translation probability form thethird language word w_(j) to the second language word w_(e),C(w_(j),w_(c)) represents the co-occurrence count of the first languageword w_(c) and the third language word w_(j), p(w_(c)|w_(j)) representsthe translation probability from the third language word w_(j) to thefirst language word w_(e),

said word translation sub-model estimating unit collects theco-occurrence count of the first language word w_(c) and the thirdlanguage word w_(j), using formula

${{C\left( {w_{j},w_{c}} \right)} = {\sum\limits_{w_{e}}\; {{p_{EJ}\left( {w_{e}\text{|}w_{j}} \right)}*{p_{CE}\left( {w_{c}\text{|}w_{e}} \right)}}}};$

and calculates the translation probability from the third language wordw_(j) to the first language word w_(c), using formula

${p\left( {w_{c}\text{|}w_{j}} \right)} = {\frac{C\left( {w_{j},w_{c}} \right)}{\sum\limits_{w_{c^{\prime}}}\; {C\left( {w_{j},w_{c^{\prime}}} \right)}}.}$

Suppose that P_(EJ)(k|i,l,m′) represents the probability that the i^(th)position in the third language sentence having a length of l iscorresponding to the k^(th) position in the second language sentencehaving a length of m′, p_(CE)(j|k,m′,m) represents the probability thatthe k^(th) position in the second language sentence having a length ofm′ is corresponding to the j^(th) position in the first languagesentence having a length of m, C(j,i,l,m) and p_(CJ)(j|i,l,m)respectively represent the co-occurrence count and probability that thei^(th) position in the third language sentence having a length of l iscorresponding to the j^(th) position in the first language sentencehaving a length of m,

said position distortion sub-model estimating unit collects theco-occurrence count that the i^(th) position in the third languagesentence having a length of l is corresponding to the j^(th) position inthe first language sentence having a length of m, using formulaC(j,i,l,m)=Σ_(k,m′)p_(EJ)(k|i,l,m′)*P_(CE)(j|k,m′, m);

and calculates the position distortion probability that the i^(th)position in the third language sentence having a length of l iscorresponding to the j^(th) position in the first language sentencehaving a length of m, using formula

${p_{CJ}\left( {{j\text{|}i},l,m} \right)} = {\frac{C\left( {j,i,l,m} \right)}{\sum\limits_{j^{\prime}}\; {C\left( {j^{\prime},i,l,m} \right)}}.}$

Suppose that p_(EJ)(w_(e)|w_(j)) represents the translation probabilityform the third language word w_(j) to the second language word w_(e)φ_(i) p_(CE)(φ_(i)|w_(e)) represents the probability that the secondlanguage word w_(e) is corresponding to φ_(i) words of the firstlanguage, C(φ_(i),w_(j)) and p(φ_(i)|w_(j)) respectively represent theco-occurrence count and probability that the third language word w iscorresponding to φ_(i) words of the first language,

said word fertility sub-model estimating unit collects the co-occurrencecount that the third language word w_(j) is corresponding to φ_(i) wordsof the first language, using formula

${{C\left( {\phi_{i},w_{j}} \right)} = {\sum\limits_{w_{e}}\; {{p_{EJ}\left( {w_{e}\text{|}w_{j}} \right)}*{p_{CE}\left( {\phi_{i}\text{|}w_{e}} \right)}}}};$

and calculates the probability that the third language word w_(j) iscorresponding to φ_(i) words of the first language, using formula

${p\left( {\phi_{i}\text{|}w_{j}} \right)} = {\frac{C\left( {\phi_{i},w_{j}} \right)}{\sum\limits_{\phi_{i}^{\prime}}\; {C\left( {\phi_{i}^{\prime},w_{j}} \right)}}.}$

From above description it can be seen that the apparatus for training abilingual word alignment model of this embodiment can operationallyrealize the method for training a bilingual word alignment model of theembodiment above described in FIG. 1. By using this embodiment, theproblem that there is no way to obtain a word alignment model with highquality due to not sufficient corpus for training can be solved throughusing an intermediate language.

Here it should be noted that the apparatus 300 for training a bilingualword alignment model of the embodiment and its components can beconstructed with dedicated circuits or chips, or can be realized by acomputer (processor) through executing corresponding programs.

Under the same inventive concept, FIG. 4 is a block diagram showing anapparatus for bilingual word alignment according to an embodiment of thepresent invention. Next, in conjunction with the figure, a descriptionwill be given to this embodiment. For the parts identical to that in theprevious embodiments, explanation will be omitted properly.

As shown in FIG. 4, the apparatus 400 for bilingual word alignment ofthis embodiment comprises: the apparatus 300 for training a bilingualword alignment model above-described in FIG. 3 and a word alignment unit406. The word alignment unit 406 word-aligns the bilingual sentencepairs in the first and third languages, using the bilingual wordalignment model for the first and third languages obtained by theapparatus 300 for training a bilingual word alignment model. Specificword alignment manner has been described in the previous embodiment andare not repeated here.

From above description it can be seen that the apparatus 400 forbilingual word alignment of this embodiment can operationally realizethe method for bilingual word alignment of above described embodiment ofthe present invention. By using the apparatus for bilingual wordalignment of this embodiment, the problem that there is no way to obtaina word alignment model with high quality due to not sufficient corpusfor training can be solved through using an intermediate language. Thus,even for those bilingual languages with less corpus, such as Chinese andEnglish, accurate word alignment can be made.

Here it should be noted that the apparatus 400 for bilingual wordalignment of the embodiment and its components can be constructed withdedicated circuits or chips, or can be realized by a computer(processor) through executing corresponding programs.

Though a method and apparatus for bilingual word alignment and a methodand apparatus for training a bilingual word alignment model of thepresent invention have been described in details with some exemplaryembodiments, these embodiments are not exhaustive. Those skilled in theart may make various variations and modifications within the spirit andscope of the present invention. Therefore, the present invention is notlimited to these embodiments, and the scope of the present invention isonly defined by the appended claims.

1. A method for training bilingual word alignment model, comprising:training a bilingual word alignment model for a first language and asecond language, using a bilingual corpus of the first and secondlanguages; training a bilingual word alignment model for the secondlanguage and a third language, using a bilingual corpus of the secondand third languages; and estimating a bilingual word alignment model forthe first language and the third language, based on said bilingual wordalignment model for the first and second languages and said bilingualword alignment model for the second and third languages.
 2. The methodfor training bilingual word alignment model according to claim 1,wherein said bilingual word alignment model for the first and secondlanguages and said bilingual word alignment model for the second andthird languages respectively comprises: a word translation sub-model, aposition distortion sub-model and a word fertility sub-model; said stepof estimating a bilingual word alignment model for the first languageand the third language comprises: estimating a word translationsub-model for the first and third languages, based on the wordtranslation sub-model for the first and second languages and the wordtranslation sub-model for the second and third languages; estimating aposition distortion sub-model for the first and third languages, basedon the position distortion sub-model for the first and second languagesand the position distortion sub-model for the second and thirdlanguages; and estimating a word fertility sub-model for the first andthird languages, based on the word fertility sub-model for the first andsecond languages and/or the word fertility sub-model for the second andthird languages, the word translation sub-model for the first and secondlanguages and/or the word translation sub-model for the second and thirdlanguages.
 3. The method for training bilingual word alignment modelaccording to claim 2, wherein said step of estimating a word translationsub-model for the first and third languages comprises: whereP_(CE)(w_(c)|w_(e)) represents the translation probability from thesecond language word w_(e) to the first language word w_(c),P_(EJ)(w_(e)|w_(j)) represents the translation probability form thethird language word w_(j) to the second language word w_(e),C(w_(j),w_(c)) represents the co-occurrence count of the first languageword w_(c) and the third language word w_(j), p(w_(c)|w_(j)) representsthe translation probability from the third language word w_(j) to thefirst language word w_(c), collecting the co-occurrence count of thefirst language word w_(c) and the third language word w_(j), usingformula${{C\left( {w_{j},w_{c}} \right)} = {\sum\limits_{w_{e}}\; {{p_{EJ}\left( {w_{e}\text{|}w_{j}} \right)}*{p_{CE}\left( {w_{c}\text{|}w_{e}} \right)}}}};$ and calculating the translation probability from the third languageword w_(j) to the first language word w_(c), using formula${p\left( {w_{c}\text{|}w_{j}} \right)} = {\frac{C\left( {w_{j},w_{c}} \right)}{\sum\limits_{w_{c^{\prime}}}\; {C\left( {w_{j},w_{c^{\prime}}} \right)}}.}$4. The method for training bilingual word alignment model according toclaim 2, wherein said step of estimating a position distortion sub-modelfor the first and third languages comprises: where p_(EJ)(k|i,l,m′)represents the probability that the i^(th) position in the thirdlanguage sentence having a length of l is corresponding to the k^(th)position in the second language sentence having a length of m′,p_(CE)(j|k,m′,m) represents the probability that the k^(th) position inthe second language sentence having a length of m′ is corresponding tothe j^(th) position in the first language sentence having a length of m,C(j,i,l,m) and P_(CJ)(j|i,l,m) respectively represent the co-occurrencecount and probability that the i^(th) position in the third languagesentence having a length of l is corresponding to the j^(th) position inthe first language sentence having a length of m, collecting theco-occurrence count that the i^(th) position in the third languagesentence having a length of l is corresponding to the j^(th) position inthe first language sentence having a length of m, using formulaC(j,i,l,m)=Σ_(k,m′)p_(EJ)(k|i,l,m′)*p_(CE)(j|k,m′,m); and calculatingthe position distortion probability that the i^(th) position in thethird language sentence having a length of l is corresponding to thej^(th) position in the first language sentence having a length of m,using formula${p_{CJ}\left( {{j\text{|}i},l,m} \right)} = {\frac{C\left( {j,i,l,m} \right)}{\sum\limits_{j^{\prime}}\; {C\left( {j^{\prime},i,l,m} \right)}}.}$5. The method for training bilingual word alignment model according toclaim 2, wherein said step of estimating a word fertility sub-model forthe first and third languages comprises: where P_(EJ)(w_(e)|w_(j))represents the translation probability form the third language wordw_(j) to the second language word w_(e), p_(CE)(φ_(i)|w_(e)) representsthe probability that the second language word w_(e) is corresponding to _(i) words of the first language, C(φ_(i),w_(j)) and p(φ_(i)|w_(j))respectively represent the co-occurrence count and probability that thethird language word w_(j) is corresponding to φ_(i) words of the firstlanguage, collecting the co-occurrence count that the third languageword w_(j) is corresponding to φ_(i) words of the first language, usingformula${{C\left( {\phi_{i},w_{j}} \right)} = {\sum\limits_{w_{e}}\; {{p_{EJ}\left( {w_{e}\text{|}w_{j}} \right)}*{p_{CE}\left( {\phi_{i}\text{|}w_{e}} \right)}}}};$ and calculating the probability that the third language word w_(j) iscorresponding to φ_(i) words of the first language, using formula${p\left( {\phi_{i}\text{|}w_{j}} \right)} = {\frac{C\left( {\phi_{i},w_{j}} \right)}{\sum\limits_{\phi_{i}^{\prime}}\; {C\left( {\phi_{i}^{\prime},w_{j}} \right)}}.}$6. A method for bilingual word alignment, comprising: obtaining abilingual word alignment model for a first language and a third languagebased on the bilingual corpus of the first and second languages and thebilingual corpus of the second and third languages, by using the methodfor training bilingual word alignment model according to any one ofclaims 1˜5; word-aligning a bilingual sentence pair of the first andthird languages using said bilingual word alignment model of the firstand third languages.
 7. An apparatus for training bilingual wordalignment model, comprising: a first training unit configured to train abilingual word alignment model for a first language and a secondlanguage, using a bilingual corpus of the first and second languages; asecond training unit configured to train a bilingual word alignmentmodel for the second language and a third language, using a bilingualcorpus of the second and third languages; and a model estimating unitconfigured to estimate a bilingual word alignment model for the firstlanguage and the third language, based on said bilingual word alignmentmodel for the first and second languages and said bilingual wordalignment model for the second and third languages.
 8. The apparatus fortraining bilingual word alignment model according to claim 7, whereinsaid bilingual word alignment model for the first and second languagesand said bilingual word alignment model for the second and thirdlanguages respectively comprises: a word translation sub-model, aposition distortion sub-model and a word fertility sub-model; said modelestimating unit comprises: a word translation sub-model estimating unitconfigured to estimate a word translation sub-model for the first andthird languages, based on the word translation sub-model for the firstand second languages and the word translation sub-model for the secondand third languages; a position distortion sub-model estimating unitconfigured to estimate a position distortion sub-model for the first andthird languages, based on the position distortion sub-model for thefirst and second languages and the position distortion sub-model for thesecond and third languages; and a word fertility sub-model estimatingunit configured to estimate a word fertility sub-model for the first andthird languages, based on the word fertility sub-model for the first andsecond languages and/or the word fertility sub-model for the second andthird languages, the word translation sub-model for the first and secondlanguages and/or the word translation sub-model for the second and thirdlanguages.
 9. The apparatus for training bilingual word alignment modelaccording to claim 8, wherein p_(CE)(w_(c)|w_(e)) represents thetranslation probability from the second language word w_(e) to the firstlanguage word w_(c), P_(EJ)(w_(e)|w_(j)) represents the translationprobability form the third language word w_(j) to the second languageword w_(e), C(w_(j),w_(c)) represents the co-occurrence count of thefirst language word w_(c) and the third language word w_(j),p(w_(c)|w_(j)) represents the translation probability from the thirdlanguage word w_(j) to the first language word w_(c), said wordtranslation sub-model estimating unit collects the co-occurrence countof the first language word w_(c) and the third language word w_(j),using formula${{C\left( {w_{j},w_{c}} \right)} = {\sum\limits_{w_{e}}\; {{p_{EJ}\left( {w_{e}\text{|}w_{j}} \right)}*{p_{CE}\left( {w_{c}\text{|}w_{e}} \right)}}}};$ and calculates the translation probability from the third language wordw_(j) to the first language word w_(c), using formula${p\left( {w_{c}\text{|}w_{j}} \right)} = {\frac{C\left( {w_{j},w_{c}} \right)}{\sum\limits_{w_{c^{\prime}}}\; {C\left( {w_{j},w_{c^{\prime}}} \right)}}.}$10. The apparatus for training bilingual word alignment model accordingto claim 8, wherein p_(EJ)(k|i,l,m′) represents the probability that thei^(th) position in the third language sentence having a length of l iscorresponding to the k^(th) position in the second language sentencehaving a length of m′, p_(CE)(j|k,m′,m) represents the probability thatthe k^(th) position in the second language sentence having a length ofm′ is corresponding to the j^(th) position in the first languagesentence having a length of m, C(j,i,l,m) and p_(CJ)(j|i,l,m)respectively represent the co-occurrence count and probability that thei^(th) position in the third language sentence having a length of l iscorresponding to the j^(th) position in the first language sentencehaving a length of m, said position distortion sub-model estimating unitcollects the co-occurrence count that the i^(th) position in the thirdlanguage sentence having a length of l is corresponding to the j^(th)position in the first language sentence having a length of m, usingformula C(j,i,l,m)=Σ_(k,m′)p_(EJ)(k|i,l,m′)*P_(CE)(j|k,m′,m); andcalculates the position distortion probability that the i^(th) positionin the third language sentence having a length of l is corresponding tothe j^(th) position in the first language sentence having a length of m,using formula${p_{CJ}\left( {{j\text{|}i},l,m} \right)} = {\frac{C\left( {j,i,l,m} \right)}{\sum\limits_{j^{\prime}}\; {C\left( {j^{\prime},i,l,m} \right)}}.}$11. The apparatus for training bilingual word alignment model accordingto claim 8, wherein P_(EJ)(w_(e)|w_(j)) represents the translationprobability form the third language word w_(j) to the second languageword w_(e), P_(CE)(φ_(i)|w_(e)) represents the probability that thesecond language word w_(e) is corresponding to φ_(i) words of the firstlanguage, C(φ_(i),w_(j)) and p(φ_(i)|w_(j)) respectively represent theco-occurrence count and probability that the third language word w_(j)is corresponding to φ_(i) words of the first language, said wordfertility sub-model estimating unit collects the co-occurrence countthat the third language word w_(j) is corresponding to φ_(i) words ofthe first language, using formula${{C\left( {\phi_{i},w_{j}} \right)} = {\sum\limits_{w_{e}}\; {{p_{EJ}\left( {w_{e}\text{|}w_{j}} \right)}*{p_{CE}\left( {\phi_{i}\text{|}w_{e}} \right)}}}};$ and calculates the probability that the third language word w_(j) iscorresponding to φ_(i) words of the first language, using formula${p\left( {\phi_{i}\text{|}w_{j}} \right)} = {\frac{C\left( {\phi_{i},w_{j}} \right)}{\sum\limits_{\phi_{i}^{\prime}}\; {C\left( {\phi_{i}^{\prime},w_{j}} \right)}}.}$12. An apparatus for bilingual word alignment comprising; modelobtaining unit configured to obtain a bilingual word alignment model fora first language and a third language based on a the bilingual corpus ofthe first and second languages and the bilingual corpus of the secondand third languages by the apparatus for training bilingual wordalignment model according to any one of claims 7˜11 and; word-alignmentunit configured to word-align a bilingual sentence pair of the first andthird languages using the bilingual word alignment model for the firstand third languages.